The Definitive Guide to Collaborative Analytics

Humans are valuable resources. Their brains are full of knowledge gained over years of experience, and their minds are capable of creativity that AI can’t match (at least not yet). This creativity based on experience is the source of smart questions and the ability to combine knowledge in ways that lead to new insights — in other words, the stuff of effective data analytics. And the benefits multiply when these minds work together so that one team’s work can build upon another’s.

Being truly data-driven requires that you leverage the knowledge, perspectives, and work of all your people — not just those with technical expertise. It demands a community-driven approach that involves stakeholders and domain experts across departments, partners, customers, applications, ecosystems, and even entire industries. For this reason, companies that are seeing the biggest benefits of being data-driven have implemented collaborative analytics and built a data-curious culture to accompany it.

If your organization is on a mission to be more data-driven, you need diverse perspectives that involve the minds of those who are closest to the meaning of the data. Ventana Research has found that nearly four in 10 organizations are using collaboration to support analytics processes — and more than half said they expect to use these capabilities in the future.

In this guide, we explore:

• what collaborative analytics is
• what’s holding companies back from fully implementing it
• the benefits you can expect from a collaborative approach
• what to look for in collaborative analytics tools
• a framework for building a collaborative analytics culture that will serve as fuel for greater insights

What is collaborative analytics?

Collaborative analytics is part of the broader movement in analytics to approach BI from a community-driven perspective. It uses a combination of business intelligence software and collaboration tools to allow a broad spectrum of people in an organization- (and beyond) to participate in data analytics.

Collaborative analytics emphasizes the problem-solving process, correctly identifying that data analysis that generates the most valuable insights doesn’t happen in a vacuum. Without the input of people who have a thorough understanding of the industry, are talking with customers, working on product development, managing production, etc., data analysts are operating without context.

What collaborative analytics includes

Functionally, collaborative analytics includes a variety of elements. It involves collaboration around the discovery, creation, sharing, and use of data assets. For example, a sales leader may realize that a particular dataset in the organization’s CRM would be valuable for a particular use, recommending to the data team that they make that data available in the analytics tool. Collaborative analytics also works the other direction — data teams make business users aware of endorsed datasets and the data resources available and how to best use them. This “best use” education may involve a formal training program on data skills, or it may happen on an informal basis, as needed. More likely, it’s a mix of both.

While human minds are an indispensable part of collaborative analytics, the process also makes use of AI. Much of AI’s potential when it comes to knowledge sharing remains to be realized, but current capabilities are valuable, including improving community exposure and streamlining processes. AI’s ability to do things like identify similar datasets in a warehouse, encourage joins, and prompt users to try different visualizations to reveal trends more effectively dramatically improves a company’s proficiency with collaborative analytics.

Tool capabilities that enable collaboration

What does collaborative analytics software look like in action? There are a few capabilities that facilitate the process.

• Team workspaces — Team workspaces governed by permissions and controls that ensure security allow employees with teams and across teams to collaborate.
• Reusable workflows — Datasets used and analyses conducted by one team are able to be saved and reused by others.
• Single source of truth — Data is centralized and available via a single access point, ensuring that everyone is using the same version of the data.
• Chat — Built-in or API-integrated collaboration tools allow team members to ask questions, make comments, and tag others for feedback.
• Visual, collaborative data modeling — A visual approach to data modeling allows business users to participate without writing code. Schemas and tables are available for all users to explore, and business users can create or contribute to new data models  and add their input to existing datasets.
  

What’s holding companies back from being community-driven

Although most organizations aspire to be collaborative, the reality is that roadblocks are preventing full adoption. Here’s what holding companies back and what they can do to remove these roadblocks.

Most popular BI tools weren’t built to be collaborative

While many legacy tools have bolted-on analytics collaboration functionality, they haven’t been built from the ground up with collaboration in mind. This results in clunky or complicated user experiences that, in practice, end up preventing business users from being as involved as they want to (and should be). To implement collaborative analytics well, you need a tool that has an easy-to-use UX that enables all types of people to explore data, not just those with SQL knowledge.

Many newer tools that aim to be collaborative only offer limited access to business users

Even many solutions that were built with collaboration tools as foundational features don’t offer business users  the ability to ask unique questions, or explore reports with set parameters. True collaboration is limited to those with technical skills on the data team. There’s no ability for business users to participate in data modeling or conduct queries beyond a limited, pre-designed sandbox where they are second-class data citizens. 

Some organizations aren’t taking advantage of cloud data warehouse capabilities that enable collaborative analytics

In order to implement collaborative analytics, you must have the right infrastructure in place with the right capabilities. Modern cloud data warehouses like Snowflake and Google BigQuery can store massive amounts of data, scale to meet analytical demands of entire organizations, and make it possible to centralize company data for holistic analysis. They are a requirement for any company seriously considering a collaborative and community-driven approach to analytics. 

Many organizations aren’t using an A&BI solution that enables a community-driven approach with protective governance

Opening up data access, exploration, and analysis without compromising security demands a modernized approach to data governance. To be truly community-driven, your analytics and BI solution must make data accessible and approachable for everyone while upholding strict compliance and security standards.

Benefits of a collaborative approach to analytics & BI

We’ve established that you need the right tools in order to truly achieve collaboration. But implementing tools is always a bit of work and requires resources. Is collaborative analytics worth it? Here are a few of the many benefits that provide a strong answer of “yes.”

Discovery of available data

Data sources and datasets can easily remain undiscovered or be overlooked without someone pointing them out to the larger team. When a broad spectrum of people serving a variety of roles is involved in the data analytics process, an organization is able to identify and share all its valuable data and put it to use. 

Better use of available data

Companies sit on a treasure trove of data. But 73% of it goes unused for analytics. When all the data relevant to a given question is brought to bear, collaborative teams can make better use of the data. Those on the data team can endorse the most relevant and accurate data, and business users understand the nuances of meaning that can be derived from it.

Fuller use of domain experts’ knowledge

When domain experts are limited to static dashboards built by the data team, the organization allows much of their knowledge to go to waste. These domain experts can ask more meaningful follow-up questions due to their on-the-ground understanding of the situation that’s the subject of the inquiry. Organizations are already paying for this knowledge — with collaborative analytics, they’re able to maximize their investment.

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More accurate answers to the “why” questions

Data teams’ expertise lies in the areas of data sourcing, processing, modeling, and analytics. They aren’t typically talking to the customers, working with the product, or spending time observing the production line. For this reason, they’re extremely adept at identifying trends and issues, but they often don’t know what questions to ask to find out why trends and issues are happening. They are, of course, able to narrow down the “why” possibilities. But past a certain point, their analysis is guesswork. They need input from those with domain expertise, who are seeing and hearing things that aren’t showing up in the data.

At times, this input can save a company from making costly errors. For example, an analyst may discover that sales have dropped in a particular region of the country while all other regions remain strong. Without input from the territory manager, the real cause will remain unknown. The company could waste millions of dollars rectifying a “problem” that doesn’t exist — only to discover that the drop was due to a different problem that requires another outlay of cash in order to solve the issue.

Faster speed to insight

The faster you can collaborate and bring all knowledge and perspectives to bear, the faster your speed to insight. Yes, a certain level of collaboration can happen without the appropriate tools and processes. But often, companies that “win” are those who are able to move quickly — before competitors. And in cases where insights are necessary to solve problems, speed can mean significant cost savings.

Generate curiosity and encourage people to look for new insights

Another major benefit is that collaborative analytics encourages curiosity. When domain experts have the ability to participate and their input is taken into account, they’re motivated to look for new insights on their own. This tendency is of great value to an organization, as the company is able to innovate and move in ways it wouldn’t be able to otherwise.

What to look for in collaborative analytics & BI software

Because many collaborative analytics tools are limited in their collaborative functionality, you’ll want to do your research before committing to one. Here’s what to look for in collaborative BI software to ensure you experience the benefits that a community-driven approach has to offer.

Full set of collaboration tools

Look for team workspaces that allow users to collaborate on analysis as individual teams and share data with other teams in the organization. Be sure that business users can easily build on each other's work and share insights using connected tools that they already use in their everyday workflows, such as Slack and email.

Robust permissions and security features

Be sure that business users won’t be hindered from fully making use of the tool in the name of security. Robust permissions and security features, alongside a balanced data governance program, allow the people who should have access to fully explore and use the tool.

Collaboration in exploration

Your collaborative analytics tool should be built with both technical and business users in mind. Technical users should be able to easily perform all their tasks in the tool. At the same time, business users should be able to experience an intuitive interface based on popular non-technical tools like Microsoft Office to explore data, create their own visualizations, contribute their perspectives, and work as equals with the data team. Your tool should allow anyone to do a deep-dive series of queries — a capability that Sigma excels in.

Reusable datasets and analyses

An essential part of collaboration is the ability to build on the work of others. Your collaborative analytics tool should allow you to reuse datasets and analyses that other teams have already added and created.

Collaborative data modeling

Before business users can make use of data, it must be modeled. Ideally, business users will collaborate with technical users on the modeling process, and for this purpose, visual data modeling capability is a must. Collaboration in a central location using a visual format that anyone can understand ensures that business users aren’t modeling data on their desktops using the Excel program installed on their hard drive, while at the same time keeping data democratized.

How to build a collaborative analytics culture

While tools are important, they’re not enough. A 2019 survey by NewVantage Partners revealed that 95% of executives said their difficulty in becoming data-driven is a result of cultural challenges around data. You can have all the best tools, from a modern cloud data warehouse to the best collaborative analytics platform, and still fail to experience the benefits of collaboration.

Beyond the tools, you need a collaborative analytics culture that encourages users to view one another with respect, to value a variety of perspectives, and to take advantage of their ability to uncover the “whys” behind the trends. Here are the primary steps you’ll need to take to build a collaborative analytics culture.

Emphasize the benefits of collaborative analytics

Showing your people just how collaborative analytics can benefit not only the company at large but also each of their teams will go a long way in generating buy-in. When team members understand the value of collaboration, they’ll be motivated to contribute and to seek the contributions of others.

Break down silos between departments

The default style of most organizations is for departments to operate independently, in silos. This is such a norm that it has become the target of jokes.

To build a collaborative analytics culture, you’ll need to disrupt this pattern. To do this, you’ll need to communicate your vision for collaboration, open up cross-departmental communications, hold cross-functional trainings, and put other measures in place designed to build trust between departments.

95% of executives said cultural challenges hindered their ability to become data-driven. 

Promote diversity of perspectives

One of the most important tasks involved in building a culture of collaboration is to promote diversity. Help everyone to understand just why other perspectives are so valuable. Point to the success of other organizations using collaboration to reach their goals. Everyone in the organization should receive a loud and clear message that each individual perspective is valued.

Encourage curiosity

Truly data-driven teams run on curiosity. They’re constantly asking the “why” questions — and following up with additional “why” questions. Encourage both your data team and your domain experts to get curious and explore data to satisfy their curiosity and generate valuable insights in the process.

Build a balanced data governance program

Data democratization doesn’t have to look like the Wild West. A data governance framework that simultaneously makes data accessible while minimizing risk will ensure that people don’t fear collaboration. The need for data governance is nothing new, and best practices have remained fairly consistent. Snowflake’s Chief Data Evangelist, Kent Graziano, aptly says, “What might be surprising to many people is that data governance best practices have existed for a couple of decades. The questions have already been answered. We now need to force ourselves into the discipline of following those best practices.”

Democratize access to data

Finally, a collaborative analytics culture depends on democratized access to data. As long as an organization restricts data access to the technical elite, domain experts will not feel that their voice is valued in the data conversation. Yes, security measures must be put in place, and processes must be followed. But business users should be able to easily use your data analytics tool — and in a broader capacity than simply making comments in a chat function and viewing pre-designed dashboards.

Learn more about how to create a culture of collaboration — read Passing the Ball: 3 Lessons in Analytics Collaboration from Basketball

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Community-driven analytics is the future

Due to the invaluable benefits of collaborative analytics, many organizations are in a race to become more community-driven. As the research shows, the majority of those companies that are not yet taking advantage of collaborative analytics processes are planning to implement collaborative analytics in the future. Only 11% of companies say they don’t plan to follow the collaborative path.

To start seeing the benefits that community-driven analytics offers and compete with organizations that are, you’ll need to start building your collaborative capabilities and culture now.